An Integrated Graph-Theoretic Approach to Understanding Solvation Using a Novel Data Mining Tool, moleculaRnetworks

Abstract

An integrated graph-theoretic and geometric approach to the analysis of aqueous solvation of atomic ions is presented. This analysis makes use of a novel data-mining tool, moleculaRnetworks, to process data from molecular dynamics simulations. The workings and structure of this tool are discussed, along with the development and testing of its PageRank algorithm-based rapid solvation polyhedra classifier. The ability to classify instantaneous solvation polyhedra enables a finely detailed understanding of shell structure-behavior relationships, as water molecules simultaneously rearrange about ions, exchange with the bulk, and rearrange their hydrogen-bond network. The application of the tool to cation systems, including lithium, sodium, potassium, magnesium, calcium, and lanthanum, yields new insight into the mechanisms of water exchange about these ions. It is shown that in order for exchange events to occur, the solvation shell must "preorganize" to admit or expel a molecule of water: this preorganization is reflected in the mechanistic preference for each ion. The application of the tool to anion systems, including fluoride, chloride, and bromide, reveals that these ions have an extended effect on the reorientation ability of water molecules beyond their first solvation shell. Finally, when both ions are present, as in the potential of mean force simulation between lanthanum and chloride, structural rearrangements can be seen as the ions break through the barrier to form the contact ion pair. Taken together, these results show the utility of the moleculaRnetworks tool in broadening our understanding of aqueous ion solvation.